The Research Group in Bioinformatics and Cheminformatics (BioChemTICs) belongs to the Universidad Nacional del Sur, a public university in Argentina. BioChemTICs is also part of the Institute for Computer Science and Engineering (ICIC), a research institute from the Argentinean National Research Council (CONICET). BioChemTICs comprises six members working in machine learning and big data applied to several applications to Computational Biology and Molecular Informatics. Our research relies on the analysis of massive amounts of data and involves the use genetic algorithms, artificial neural networks and visual analytics.

BioChemTICs Team

Head: Ignacio Ponzoni

Staff: Jessica A. Carballido, Rocío L. Cecchini, Axel J. Soto

Posdoctoral Fellows: María Jimena Martínez

PhD Students: María Virginia Sabando



  • Cravero, F., Schustik, S., Martínez, M.J., Barranco, C.D., Díaz, M.F., Ponzoni, I. “Computer-Aided Design of Polymeric Materials: Characterization of Databases for Prediction of Mechanical Properties under Polydispersity”. Chemometrics and Intelligent Laboratory Systems, Vol. 191, pp. 65-72 (2019). Elsevier. ISSN: 0169-7439.
  • Ponzoni I., Sebastian-Pérez V., Martínez M.J., Roca C., De la Cruz, C., Cravero F., Vazquez, G.E., Páez J.A., Díaz M.F., Campillo N.E. “QSAR Classification Models for Predicting the Activity of Inhibitors of Beta-Secretase (BACE1) Associated with Alzheimer’s Disease”. Scientific Reports, Vol. 9:9102, (2019). Nature Pub. Group. ISSN: 2045-2322.
  • Díaz–Montaña, J.J., Díaz–Díaz, N., Barranco, C.D., Ponzoni, I. “Development and use of a Cytoscape app for GRNCOP2”, Computer Methods and Programs in Biomedicine. Vol. 177, pp. 211–218 (2019). Elsevier. ISSN: 0169-2607.
  • Sebastián-Pérez, V., Martínez, M.J., Gil, C., Campillo, N.E., Martínez, A., Ponzoni, I. “Inference of QSAR Models for identifying LRRK2 inhibitors for the treatment of Parkinson’s Disease», Journal of Integrative Bioinformatics. Vol. 16, Issue 1 (2019). De Gruyter. ISSN: 1613-4516.
  • Martínez, M.J., Razuc, M., Ponzoni I. “MoDeSuS: A Machine Learning Tool for Selection of Molecular Descriptors in QSAR Studies applied to Molecular Informatics”, BioMed International Research. Vol. 2019, Article number 2905203 (2019). Hindawi. ISSN: 2314-6133.
  • Cecchini, R.L., Lorenzetti, C.M., Maguitman, A.G., Ponzoni, I. “Topic Relevance and Diversity in Information Retrieval from Large Datasets: A Multi-Objective Evolutionary Algorithm Approach», Applied Soft Computing. Vol. 69, pp. 749-770 (2018). Elsevier. ISSN: 1568-4946.
  • Martínez M.J., Dussaut J.S., Ponzoni, I. “Biclustering as Strategy for Improving Feature Selection in Consensus QSAR Modeling”, Electronic Notes in Discrete Mathematics, Vol. 69, pp. 117-124 (2018). Elsevier. ISSN: 1571-0653.
  • Dussaut J.S., Cecchini, R.L., Gallo, C.A., Ponzoni, I., Carballido, J.A. “A Review of Software Tools for Pathway Crosstalk Inference”, Currents Bioinformatics, 13 (1), pp. 64-72 (2018). Bentham Science.ISSN: 1574-8936.
  • Carballido, J.A., Latini M.A., Ponzoni, I., Cecchini, R.L. “An Evolutionary Algorithm for Automatic Recommendation of Clustering Methods and its Parameters”, Electronic Notes in Discrete Mathematics, Vol. 69, pp. 229-236 (2018). Elsevier. ISSN: 1571-0653.
  • Dussaut J.S., Gallo, C.A., Martínez M.J., Cravero F., Carballido, J.A., Ponzoni, I. “GeRNet: A Gene Regulatory Network Tool”, Biosystems. Vol. 162, pp. 1-11, (2017). Elsevier. ISSN: 0303-2647.
  • Ponzoni I., Sebastian-Pérez, Requena C., Roca C., Martínez M.J., Cravero F., Díaz M.F., Páez J.A., Gomez Arrayas R., Adrio J., Campillo N.E. “Hybridizing Feature Selection and Feature Learning Approaches in QSAR Modeling for Drug Discovery”, Scientific Reports. Vol. 7:2403, (2017). Nature Pub. Group. ISSN: 2045-2322.
  • Cravero F., Martínez M.J., Vázquez G., Díaz M., Ponzoni I. “Feature Learning applied to the Estimation of Tensile Strength at Break in Polymeric Material Design”, Journal of Integrative Bioinformatics. Vol. 13, No. 2, 286 (2016). De Gruyter. ISSN: 1613-4516.
  • Romero J.R., Carballido J.A., Garbus I., Echenique V.C., Ponzoni I. “A Bioinformatics Approach for Detecting Repetitive Nested Motifs using Pattern Matching”, Evolutionary Bioinformatics. Vol. 12, pp. 247-251, (2016). SAGE Publishing. ISSN: 1176-9343.
  • Gallo, C.A., Cecchini, R.L., Carballido, J.A., Micheletto, S., Ponzoni, I. “Discretization of gene expression data revised”, Briefings in Bioinformatics. Vol. 17 (5), pp. 758-770, (2016). Oxford University Press. ISSN: 1467-5463.
  • Dussaut J.S., Gallo, C.A., Cecchini, R.L., Carballido, J.A., Ponzoni, I. “Crosstalk Pathway Inference using Topological Information and Biclustering of Gene Expression Data”, Biosystems, Vol.150, pp. 1-12, (2016). Elsevier. ISSN: 0303-2647.
  • Martínez, M.J., Ponzoni, I., Díaz, M.F., Vazquez, G.E., Soto, A.J. “Visual analytics in cheminformatics: user‑supervised descriptor selection for QSAR methods”, Journal of Cheminformatics, Vol. 7, Paper 39, (2015). Springer. ISSN 1758-2946.
  • Carballido, J.A., Gallo, C.A., Dussaut J.S., Ponzoni, I. “On Evolutionary Algorithms for Biclustering of Gene Expression Data”, Currents Bioinformatics, Vol. 10, No. 3, pp. 259-267, (2015). Bentham Science.ISSN: 1574-8936.
  • Ponzoni I., Nueda M.J., Tarazona S., Götz S., Montaner D., Dussaut J.S., Dopazo J., Conesa A. “Pathway network inference from gene expression data”, BMC Systems Biology, Vol. 8, S7. Springer Nature, (2014).ISSN: 1752-0509.
  • Romero, J.R., Roncallo, P.F., Akkiraju, P.C., Ponzoni, I., Echenique, V.C., Carballido, J.A. “Using classification algorithms for predicting durum wheat yield in the province of Buenos Aires”, Computers and Electronics in Agriculture, Vol. 96, pp. 173-179. Elsevier, (2013). ISSN: 0168-1699.
  • Palomba, D., Martínez, M.J., Ponzoni, I., Díaz, M.F., Vazquez, G.E., Soto, A.J. “QSAR models for predicting log Pliver on volatile organic compounds combining statistical methods and domain knowledge”, Molecules, Vol. 17, No. 12, pp. 14937-14953. MDPI AG, (2012). ISSN 1420-3049.
  • Cecchini, R.L., Ponzoni, I., Carballido, J.A. “Multi-objective evolutionary approaches for intelligent design of sensor networks in the petrochemical industry”, Expert Systems with Applications, Vol. 39, pp. 2643-2649, Elsevier, (2012). ISSN: 0957-4174.
  • Soto, A.J., Vazquez, G.E., Strickert, M., Ponzoni, I. “Target-driven subspace mapping methods and their applicability domain estimation”, Molecular Informatics, Vol. 30, pp. 779–789, Wiley, (2011). ISSN: 1868-1751. 10.1002/minf.
  • Gallo, C.A., Carballido, J.A., Ponzoni, I. “Discovering Time-Lagged Rules from Microarray Data using Gene Profile Classifiers”, BMC Bioinformatics. Vol. 12, paper 123, Springer Nature, (2011). ISSN: 1471-2105.
  • Soto, A.J., Cecchini, R.J., Vazquez, G.E., Ponzoni, I. “Multi-Objective Feature Selection in QSAR/ QSPR using a Machine Learning Approach”, QSAR & Combinatorial Science. Vol. 28, No. 11-12, pp. 1509-1523. Wiley, (2009). ISSN: 1611-020X.
  • Carballido, J.A., Ponzoni, I., Brignole, N.B. “SID-GA: an Evolutionary Approach for improving Observability and Redundancy Analysis in Structural Instrumentation Design”. Computers & Industrial Engineering. Vol. 56, No. 4, pp. 1419-1428, (2009). Elsevier. ISSN: 0360-8352.
  • Domancich, A.O., Durante, M., Ferraro, S., Hoch, P., Brignole N.B., Ponzoni I. “How To Improve the Model Partitioning in a DSS for Instrumentation Design”, Industrial & Engineering Chemistry Research. Vol. 48, No. 7, pp. 3513-3525, (2009). American Chemical Society. ISSN: 0888-5885.
  • Ponzoni, I., Azuaje, F.J., Augusto, J.C., Glass, D.H. “Inferring adaptive regulation thresholds and association rules from gene expression data through combinatorial optimization learning”. IEEE/ACM Transactions on Computational Biology and Bioinformatics. Vol. 4, No. 4, pp. 624-634 (2007). IEEE Computer Society. ISSN: 1545-5963.
  • Carballido, J.A., Ponzoni, I., Brignole, N.B. “CGD-GA: A Graph-based Genetic Algorithm for Sensor Network Design”. Information Sciences. Vol. 177, No. 22, pp. 5091–5102 (2007). Elsevier. ISSN: 0020-0255.